System And Method For Intelligent Data Center Power Management And Energy Market Disaster Recovery

ABSTRACT

Systems and methods for intelligent data center power management and energy market disaster recovery comprised of data collection layer, infrastructure elements, application elements, power elements, virtual machine elements, analytics/automation/actions layer, analytics or predictive analytics engine, automation software, actions software, energy markets analysis layer and software and intelligent energy market analysis elements or software. Plurality of data centers employ the systems and methods comprised of a plurality of Tier 2 data centers that may be running applications, virtual machines and physical computer systems to enable data center and application disaster recovery from utility energy market outages. Systems and methods may be employed to enable application load balancing and data center power load balancing across a plurality of data centers and may lead to financial benefits when moving application and power loads from one data center location using power during peak energy hours to another data center location using power during off peak hours.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of patent application Ser. No.16/596,669 filed on Oct. 8, 2019, which in turn is a Continuation ofpatent application Ser. No. 14/542,011 filed on Nov. 14, 2014, entitled“A system and method for intelligent data center power management andenergy market disaster recovery” which in turn claims reference toProvisional Patent application No. 61/925,540 filed on Jan. 8, 2014,entitled “A system and method for intelligent data center powermanagement and energy market disaster recovery” the contents of whichare incorporated by reference in their entirety.

FIELD

The present invention relates to intelligent power management and datarecovery facilities.

BACKGROUND OF THE INVENTION

A data center is a facility designed to house, maintain, and power aplurality of computer systems. The computer systems within the datacenter are generally rack-mounted where a number of electronics unitsare stacked within a support frame.

A conventional Tier 4 data center is designed with 2N+1 redundancy forall power distribution paths. This means that each power distributioncomponent is redundant (2 of each component) plus there is anothercomponent added for another layer of redundancy. Essentially, if N isthe number of components required for functionality, then 2N would meanyou have twice the number of components required. The +1 means not onlydo you have full redundancy (2N), but you also have a spare, i.e., youcan take any component offline and still have full redundancy. With thisdesign you can lose one of the three components but still retain fullredundancy in case of failover. Building a Tier 4 data center is costprohibitive due to the additional power distribution components thatmust be purchased to provide 2N+1 redundancy for all power distributionpaths.

A conventional Tier 2 data center is designed with a single powerdistribution path with redundant power distribution components. Tier 2data centers can be built with lower capital expenses but do not offerthe same level of redundancy that many businesses running criticalsystems and applications require.

The described system and method for intelligent data center powermanagement and energy market disaster recovery may employ continuouscollection, monitoring and analysis of data from application services,power distribution components, virtual machines, data center facilityinfrastructure and utility energy markets to enable dynamic data centeroperation actions for migrating application loads and power loads fromone data center to another without the need for manual intervention. Thesystem and method may enable data center and application disasterrecovery from utility energy market outages by quickly migratingapplications loads from one data center location to another data centerlocation.

SUMMARY

A computer automated system for intelligent power management, comprisinga processing unit coupled to a memory element, and having instructionsencoded thereon, which instructions cause the system to, via acollection layer, collect infrastructure data, application data, powerdata, and machine element data from a plurality of correspondinginfrastructure elements, application elements, power elements, andvirtual machine elements, respectively, and further cause the system toanalyze the collected data by a single or plurality of analytic engines;and trigger, based on the analyzed collected data, a single or pluralityof operational state changes.

In a computer automated system for intelligent power management andcomprising a processing unit coupled to a memory element havinginstructions encoded thereon, a method comprising, via a collectionlayer, collecting infrastructure data, application data, power data, andmachine element data from a plurality of corresponding infrastructureelements, application elements, power elements, and virtual machineelements, respectively, analyzing the collected data by a single orplurality of analytic engines; and further comprising triggering, basedon the analyzed collected data, a single or plurality of operationalstate changes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a logical view of intelligent data center powermanagement.

FIG. 2 illustrates a logical view of an embodiment employed in a datacenter.

DETAILED DESCRIPTION OF THE INVENTION

As stated above, A data center is a facility designed to house,maintain, and power a plurality of computer systems. The computersystems within the data center are generally rack-mounted where a numberof electronics units are stacked within a support frame.

A conventional Tier 4 data center is designed with 2N+1 redundancy(where N is the number of power distribution components) for all powerdistribution paths, meaning each power distribution component isredundant (2 of each component) plus there is another component addedfor another layer of redundancy. With this design you can lose one ofthe three components but still retain full redundancy in case offailover. Building a Tier 4 data center is cost prohibitive due to theadditional power distribution components that must be purchased toprovide 2N+1 redundancy for all power distribution paths.

A conventional Tier 2 data center is designed with a single powerdistribution path with redundant power distribution components. Tier 2data centers can be built with lower capital expenses but do not offerthe same level of redundancy that many businesses running criticalsystems and applications require. Embodiments of the invention disclosedbelow solve this problem.

The system and method described may be employed to provide Tier 4 typelevels of data center power redundancy in data centers built to Tier 2standards. This drastically cuts capital expenses while providing thebenefits of Tier 4 type levels of data center power redundancy.

The claimed invention differs from what currently exists. Embodimentsdisclosed include an improved and superior system and method. Thedisclosed embodiments may be employed to provide Tier 4 type levels ofpower distribution redundancy in data centers built to Tier 2 standards.Furthermore, the systems and methods described include means tocontinuously monitor and analyze utility energy market status and enableintelligent application and data center load balancing that may providefinancial benefits for moving applications and power loads from one datacenter location using power during peak energy hours to another datacenter location using power during off-peak hours. The described systemsand methods may quickly move applications and power loads from one datacenter to another enabling disaster recovery from utility energy marketoutages.

Embodiments disclosed include improved and superior systems and methods.The claimed invention differs from what currently exists. The disclosedsystems and methods may be employed to provide Tier 4 type levels ofpower distribution redundancy in data centers built to Tier 2 standards.Furthermore, in preferred embodiments, the systems and methods describedmay continuously monitor and analyze utility energy market status andenable intelligent application and data center load balancing that mayprovide financial benefits for moving applications and power loads fromone data center location using power during peak energy hours to anotherdata center location using power during off-peak hours. The describedsystems and methods may quickly move applications and power loads fromone data center to another enabling disaster recovery from utilityenergy market outages.

Tier 2 data centers are not designed to provide Tier 4 type levels ofredundancy and may not have the ability to easily migrate applicationsor power loads from data center to data center. This may prohibitintelligent power management across data centers and the ability fordisaster recovery from utility energy market outages.

Embodiments disclosed include systems and methods for intelligent datacenter power management and energy market disaster recovery, and mayemploy continuous collection, monitoring and analysis of data fromapplication services, power distribution components, virtual machines,data center facility infrastructure and utility energy markets to enabledynamic data center operation actions for migrating application loadsand power loads from one data center to another without the need formanual intervention. The system and method may enable data center andapplication disaster recovery from utility energy market outages byquickly migrating applications loads from one data center location toanother data center location

FIG. 1 illustrates a logical view of intelligent data center powermanagement. The system comprises a data collection layer 100, a singleor plurality of infrastructure elements 102, a single or plurality ofapplication elements 104, a single or plurality of power elements 106, asingle or plurality of virtual machine elements 108, an analytics,automation, and actions layer 110 that comprises an analytics engine112, an automation engine 114, and an action engine 116, an energymarket analysis layer 118, and intelligent market elements 120. In thesystem, the data collection layer is caused to collect infrastructuredata from a single or plurality of infrastructure elements 102,application data from a single or plurality of application elements 104,power data from a single or plurality of power elements 106, and virtualmachine data from a single or plurality of virtual machine elements 108.A preferred embodiment also includes an analytics, automation, andactions layer 110, which comprises a single or plurality of analyticsengines 112, a single or plurality of automation software engines 114,and a single or plurality of actions software engines 116. Theembodiment further includes an energy market analysis engine 118, and anetwork connection to a single or plurality of energy markets 120

One embodiment of the described system and method is shown in FIG. 1(logical view) and FIG. 2 (logical data center view).

FIG. 1 shows a logical view entailed in an embodiment. An embodimentcomprises a collection layer 100, infrastructure elements 102,application elements 104, power elements 106, virtual machineelements108, analytics/automation/actions layer 110, analytics engine112, automation software 114, actions software 116, energy marketsanalysis layer 118 and intelligent energy market 120 elements.

FIG. 2 shows a logical view of an embodiment employed in a data center.The illustrated embodiment includes systems and methods comprising of aplurality of Tier 2 data centers 200, 202, 204 that may all be runningapplications, virtual machines, and the described systems and methods,global energy markets 206 and an IP network 208. According to anembodiment, data collection layer 100 continuously collects data from aplurality of infrastructure elements 102, application elements 104,power elements 106 and virtual machine elements 108. The data collectedis then analyzed by a plurality of analytic engines 112 with theresulting data analysis triggering the automation software 114 andenabling the actions software 116 to make data center operational statechanges for application load balancing or power load balancing acrossmultiple data centers 200, 202, 204. Preferably, the data centers 200,202, 204 are connected to one another by IP network 208 which may alsoconnect to a plurality of energy markets. The energy market analysislayer 118 will use data collected from energy market 206 elements toautomatically manage data center and application disaster recovery fromutility energy market 206 outages.

According to an embodiment, data collected is used to measure orquantify parameters, and if these parameters fall within definedacceptable ranges, the logic causes the system to go to the nextparameter. If the next parameter falls outside of the predefinedacceptable ranges, defined actions will be executed to bring the saidparameter within the acceptable range. For example, if the power load isgreater than the power supply, the load is reduced or the supply isincreased, to conform to a predefined range. After execution of thedefined action, (in this case the power load and supply), the data forthe same parameter will be collected again, the parameter will bechecked again, and if the parameter now falls within the acceptablerange, then the logic causes the system to move to the next parameter.

According to an embodiment the system and method includes means forintelligent management of data center power distribution loads,application loads and virtual machine loads, across multiple datacenters. An embodiment includes a computer automated system comprising aprocessing unit coupled with a memory element, and having instructionsencoded thereon, which instructions cause the system to automaticallyhandle automated data center operation state changes, and to dynamicallybalance power loads and application loads across multiple data centers.The system further includes an analysis engine which comprisesinstructions that cause the system to collect and analyze data from aplurality of energy markets, and to enable automatic data centeroperation state changes, thereby enabling data center and applicationdisaster recovery from utility energy market outages.

All of the elements above are necessary.

An additional, alternate embodiment includes a predictive analyticsengine comprising instructions that cause the system to model and toenable scenario modeling for and of designated applications, virtualmachines, and power loads. Preferred embodiments can thus predictoutages caused by energy market failures, application loads, virtualmachine loads or power loads in a data center.

Yet another embodiment includes a system and method for automaticallymanaging virtual machine instances, enabling the killing of virtualservers or banks of physical computer systems during low applicationloads and turning up virtual machines or banks of physical computersystems prior to expected peak loads.

The method and system may be deployed in a single central location tomanage multiple data center's locations. Modifications and variations ofthe above are possible, and in some instances desirable, as would beapparent to a person having ordinary skill in the art.

Preferred embodiments disclosed can be employed to enable Tier 4 typelevel redundancy to existing Tier 2 data centers. Preferred embodimentscan enable load balancing of applications and power loads acrossmultiple existing data centers.

The described systems and methods may be employed to enable disasterrecovery across multiple data centers for utility energy market outages.

Additionally: In another embodiment the systems and methods may be usedfor dynamic problem resolutions for applications, virtual machines,physical computer systems, network connectivity. The systems and methodsmay also be employed to analyze data center operation state before andafter scheduled maintenance changes and may uncover unknowninterdependencies or unanticipated changes in behavior.

The power management and energy market disaster recovery system andmethod is highly reconfigurable, and can be adapted for use in officebuildings, residential homes, schools, government buildings, cruiseships, naval vessels, mobile homes, temporary work sites, remote worksites, hospitals, apartment buildings, etc. Other variations,modifications, and applications are possible, as would be apparent to aperson having ordinary skill in the art.

Additionally, partial or complete embodiments of the disclosed inventioncan be utilized in alternate applications without departing from thescope and spirit of the disclosure. For example, the power managementand energy market disaster recovery system and method is highlyreconfigurable and can be used in a variety of situations/applications,including but not limited to buildings or dwellings, in anenergy-efficient and cost-effective manner.

Embodiments disclosed allow intelligent data center power management andenergy market disaster recovery, employing continuous collection,monitoring and analysis of data from application services, powerdistribution components, virtual machines, data center facilityinfrastructure and utility energy markets to enable dynamic data centeroperation actions for migrating application loads and power loads fromone data center to another without the need for manual intervention.Embodiments disclosed further enable data center and applicationdisaster recovery from utility energy market outages by quicklymigrating applications loads from one data center location to anotherdata center location.

Since various possible embodiments might be made of the above invention,and since various changes might be made in the embodiments above setforth, it is to be understood that all matter herein described or shownin the accompanying drawings is to be interpreted as illustrative andnot to be considered in a limiting sense. Thus, it will be understood bythose skilled in the art that although the preferred and alternateembodiments have been shown and described in accordance with the PatentStatutes, the invention is not limited thereto or thereby.

The figures illustrate the architecture, functionality, and operation ofpossible implementations of systems and methods according to variousembodiments of the present invention. It should also be noted that, insome alternative implementations, the functions noted/illustrated mayoccur out of the order noted in the figures. For example, two blocksshown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

In general, the steps executed to implement the embodiments of theinvention, may be part of an automated or manual embodiment, andprogrammable to follow a sequence of desirable instructions.

The present invention and some of its advantages have been described indetail for some embodiments. It should be understood that although someexample embodiments of the power management and energy market disasterrecovery system and method are described with reference to a waterbornedata center, the system and method is highly reconfigurable, andembodiments include reconfigurable systems that may be dynamicallyadapted to be used in other contexts as well. It should also beunderstood that various changes, substitutions and alterations can bemade herein without departing from the spirit and scope of the inventionas defined by the appended claims. An embodiment of the invention mayachieve multiple objectives, but not every embodiment falling within thescope of the attached claims will achieve every objective. Moreover, thescope of the present application is not intended to be limited to theparticular embodiments of the process, machine, manufacture, andcomposition of matter, means, methods and steps described in thespecification. A person having ordinary skill in the art will readilyappreciate from the disclosure of the present invention that processes,machines, manufacture, compositions of matter, means, methods, or steps,presently existing or later to be developed are equivalent to, and fallwithin the scope of, what is claimed. Accordingly, the appended claimsare intended to include within their scope such processes, machines,manufacture, compositions of matter, means, methods, or steps.

What is claimed is:
 1. A computer automated system for intelligent powermanagement, comprising a processing unit coupled to a memory element,and having instructions encoded thereon, which instructions cause thesystem to: trigger a collection of infrastructure data, applicationdata, power data, and machine element data; via a predictive analyticsengine, model and enable scenario modeling for and of designatedapplications, virtual machines, and power loads, based on the collectedinfrastructure data, application data, power data and machine elementdata; based on the modeled and enabled scenario modelling of thedesignated applications, virtual machines and power loads, trigger aninfrastructure load balancing, an application load balancing and a powerload balancing by the processing unit, across a plurality of datacenters by real-time migration of an infrastructure load, an applicationload and a power load from one data center to another; and wherein thesystem is configured to allow each data center to communicate with eachother data center over a network and to connect to a plurality of energyproviders over the network.
 2. The computer automated system of claim 1wherein the system is further caused to: based on the scenario modeling,analyze the collected infrastructure data, application data, power data,and machine element data by an analytic engine comprised in the computerautomated system; and based on the analyzed infrastructure data, triggeran application, power, and machine element load balancing by theprocessing unit.
 3. The computer automated system of claim 1 wherein thesystem is further caused to: based on the scenario modelling,automatically in real-time, manage data center and application disasterrecovery from utility energy market outages based on data collected fromthe plurality of energy providers over the network.
 4. The computerautomated system of claim 1 wherein the system is further caused to:based on the collected data, measure a plurality of parameters whereinif the measured plurality of parameters fall outside of a predefinedrange, execute a predefined action to bring the said measured pluralityof parameters within the predefined range.
 5. The computer automatedsystem of claim 1, wherein the system is further configured to: managein real time, data center power distribution loads, application loadsand virtual machine loads, across multiple data centers via thepredictive analytics engine scenario modeling.
 6. The computer automatedsystem of claim 1 wherein the system is further caused to: predictivelyinitiate datacenter operation state changes to balance infrastructureloads, power loads and application loads across multiple datacenters. 7.The computer automated system of claim 1 wherein the computer system isfurther configured to: analyze data collected from the plurality ofenergy providers over the network; via the predictive analytics engine,scenario model a datacenter operation state change; and based on themodelled scenario, automatically initiate the datacenter operation statechange.
 8. The computer automated system of claim 1 wherein the systemis further caused to: via the predictive analytics engine, predictoutages caused by energy provider failures to pre-empt real-time back-upor migration of infrastructure loads, application loads, virtual machineloads or power loads in a data center.
 9. The computer automated systemof claim 1 wherein the instructions further cause the system to: via thepredictive analytics engine, predictively manage virtual machineinstances, which comprises killing of virtual servers or banks ofphysical computer systems prior to low application loads and turning upvirtual machines or banks of physical computer systems prior to expectedpeak loads.
 10. In a computer automated system for intelligent powermanagement and comprising a processing unit coupled to a memory elementhaving instructions encoded thereon, a method comprising: triggering acollection of infrastructure data, application data, power data, andmachine element data; via a predictive analytics engine, modeling andenabling scenario modeling for and of designated applications, virtualmachines, and power loads, based on the collected infrastructure data,application data, power data and machine element data; based on themodeled and enabled scenario modelling of the designated applications,virtual machines and power loads, triggering an infrastructure loadbalancing, an application load balancing and a power load balancing bythe processing unit, across a plurality of data centers by real-timemigration of an infrastructure load, an application load and a powerload from one data center to another; and wherein the system isconfigured to allow each data center to communicate with each other datacenter over a network and to connect to a plurality of energy providersover the network.
 11. The method of claim 10 further comprising: basedon the scenario modeling, analyzing the collected infrastructure data,application data, power data, and machine element data by an analyticengine comprised in the computer automated system; and based on theanalyzed infrastructure data, triggering an application, power, andmachine element load balancing by the processing unit.
 12. The method ofclaim 10 further comprising: based on the scenario modelling,automatically in real-time, managing data center and applicationdisaster recovery from utility energy market outages based on datacollected from the plurality of energy providers over the network. 13.The method of claim 10 further comprising: based on the collected data,measuring a plurality of parameters wherein if the measured plurality ofparameters fall outside of a predefined range, executing a predefinedaction to bring the said measured plurality of parameters within thepredefined range.
 14. The method of claim 10, further comprising:managing in real time, data center power distribution loads, applicationloads and virtual machine loads, across multiple data centers via thepredictive analytics engine scenario modeling.
 15. The method of claim10 further comprising: predictively initiating datacenter operationstate changes to balance infrastructure loads, power loads andapplication loads across multiple datacenters.
 16. The method of claim10 further comprising: analyzing data collected from the plurality ofenergy providers over the network; via the predictive analytics engine,scenario modeling a datacenter operation state change; and based on thescenario modeling, automatically initiating the datacenter operationstate change.
 17. The method of claim 10 further comprising: predictingoutages caused by energy provider failures to pre-empt real-time back-upor migration of infrastructure loads, application loads, virtual machineloads or power loads in a data center.
 18. The method of claim 10further comprising: via the predictive analytics engine, predictivelymanaging virtual machine instances, which comprises killing of virtualservers or banks of physical computer systems prior to low applicationloads and turning up virtual machines or banks of physical computersystems prior to expected peak loads.